基于DPSIR-BP神经网络的中小河流水资源区间多阶段优化配置模型  

DPSIR-BP Neural Network Based Section Multi-stage Optimal Configuration Model of Water Resources in Middle and Small Rivers

作　　者：华杰 HUA Jie(China Railway Water Conservancy&Hydropower Planning and Design Group Co.,Ltd.,Nanchang 330029,China)

机构地区：[1]中铁水利水电规划设计集团有限公司,江西南昌330029

出　　处：《微型电脑应用》2024年第7期200-204,共5页Microcomputer Applications

摘　　要：为了合理配置水资源,提高水资源利用率,改善水资源不足的问题,研究一种基于DPSIR-BP神经网络的中小河流水资源区间多阶段优化配置模型。通过DPSIR模型构建评价指标体系,计算地区的水资源承载力,参考等级划分表,划分水资源供应区间。以用水综合效益最大值作为丰水期的目标函数,以水资源供需差值最小化为枯水期的目标函数。在供水能力、需水量变化以及变量非负值3个约束条件下,通过BP神经网络求取模型的最优解,得出中小河流水资源区间多阶段优化配置方案。结果表明,所研究模型设计的多阶段优化配置方案应用下,用水综合效益最大,水资源供需差值最小,完成多阶段优化配置。In order to rationally allocate water resources and improve the utilization rate of water resources and the shortage of water resources,a multi-stage optimal configuration model of water resources section of medium and small rivers based on DPSIR-BP neural network is studied.An evaluation index system is established through the DPSIR model to calculate the water resources carrying capacity of the region,and the water resources supply range is divided by referring to the grade division table.The maximum comprehensive benefit of water use is taken as the objective function in the wet season,and the minimum difference between supply and demand of water resources is taken as the objective function in the dry season.Under the three constraints of water supply capacity,water demand change and non-negative value of variables,the optimal solution of the model is obtained through BP neural network,and the multi-stage optimal allocation scheme of water resources section in medium and small rivers is obtained.The results show that,the multi-stage optimal allocation scheme designed by the studied model has the largest comprehensive benefit of water use and the smallest difference between supply and demand of water resources,thus completing the multi-stage optimal allocation.

关 键 词：DPSIR BP神经网络 中小河流 水资源区间 多阶段优化配置 

分 类 号：TP111.22[自动化与计算机技术—控制理论与控制工程]